Job Description
Join the Architects of Tomorrow. Nexus Future Labs is revolutionizing the way humanity interacts with artificial intelligence. We are on a mission to define the technological landscape of 2026 and beyond. We are seeking a visionary Senior AI Engineer to lead the development of our next-generation generative models and autonomous agent frameworks.
As a key member of our elite R&D division, you will not just write code; you will shape the future of machine learning. You will work with state-of-the-art infrastructure to solve complex problems at the intersection of deep learning, natural language processing, and scalable distributed systems.
Why Join Us?
- Work on cutting-edge projects that will define the industry standard.
- Competitive compensation package including equity and health benefits.
- Flexible remote-first culture with a hub in the heart of San Francisco.
Responsibilities
- Model Architecture: Design, train, and fine-tune large-scale transformer models and generative AI systems optimized for high-throughput inference.
- Research & Development: Conduct rigorous research to push the boundaries of current AI capabilities, specifically focusing on reasoning, multi-modal understanding, and autonomous agents.
- System Optimization: Engineer efficient data pipelines and model compression techniques to ensure deployment on edge devices and cloud environments.
- Leadership: Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence within the team.
- Cross-Functional Collaboration: Partner with product managers and software engineers to translate advanced research into scalable production-ready products.
- Performance Monitoring: Continuously monitor model performance, accuracy, and latency to drive iterative improvements.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: 5+ years of professional experience in deep learning, with a strong portfolio of published research or production-level AI applications.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow; deep understanding of transformer architectures (BERT, GPT, LLaMA).
- Tools: Experience with distributed training frameworks (Ray, Horovod) and cloud platforms (AWS, GCP, or Azure).
- Problem Solving: Exceptional analytical and problem-solving skills with a keen attention to detail in algorithmic optimization.
- Communication: Ability to communicate complex technical concepts to non-technical stakeholders and cross-functional teams effectively.